Oriented Connectivity Method

The oriented connectivity method of finding loops in a solar corona image is described in a paper by Jake Lee, Timothy Newman, and G. Allen Gary. (Ref.: Lee, Newman, and Gary, "Automated Detection of Solar Loops by the Oriented Connectivity Method." 17th International Conference on Pattern Recognition, Vol. 4. Cambridge, UK. 23-26 August, 2004. pp 315-318.) Below I have outlined the algorithm steps for four stages of loop detection that are detailed in this paper: 1) image cleaning, the Strous loop pixel labelling algorithm, the oriented connectivity method, and post-processing smoothing and edge linking. All algorithm credit belongs to Lee, Newman, and Gary.

Pre-Processing: Clean Coronal Image

Next, use an 11 x 11 linear filter to create a blurred image of the original

Apply the unsharp mask to subtract the blurred image from the median filtered image in the previous step, producing an image with both reduced impulse noise and sharpened edges.

Apply a global thresholding, where the threshold Tg is the median intensity of the filtered images (all 3 filtered images – median filtered, linear filtered, and unsharped, or just the final unsharped image? – ECA). Pixels with intensity less than Tg are designated as non-loop pixels.

Examine the intensities of the pixel’s 4 crosspairs (crosspairs are north-south, west-east, northwest-southeast, and southwest-northeast of pixel).

Algorithm has 2 modes: either label central pixel as loop pixel if 2 or more crosspairs are darker than the central pixel, OR label central pixel as loop pixel if 1 or more crosspairs are darker than the central pixel.

Repeat for all pixels.

Oriented Connectivity Method: Find Loops

Select an unassigned but identified loop pixel Pi to begin a new loop. (Note: save loop pixel’s coordinates so that the start and end coordinates of the loop can be established once the whole loop has been identified. - ECA)

Lee et al suggest searching for pixels that are the starting points of loops by scanning the image first column-wise and then row-wise.

Define a fan-shaped searching region for pixel Pi with extent d and bounded by Pi’s minimum and maximum azimuths. Lee et al suggest 5 pixels for d. Azimuths can be taken from a magnetogram.

Identify the unassigned loop pixels in the search region for Pi. If there are no unassigned loop pixels in the region, go back to step 2. If there are 1 or more unassigned loop pixels in the region, go to the next step.

Apply a weighting scheme to determine how likely each pixel is to be part of the loop.